The performance of driver gaze detection by video-based eye-tracking often encounters problems in lowcomputing speed, high-power consumption, and installation space constraints inside the vehicle. In this paper, we present an eye-tracking system that uses a single field-programmable-gate-array chip to overcome the aforementioned problems. In the detection system, the image quality is 640 \(\times\) 480 pixels with an 80 fps frame rate. Eye feature extraction is conducted using the enhanced semantics-based vague image representation approach. A succinct fully-connected neural network is then employed to classify various directions of sightline. Our experimental results exhibited a noticeable recognition speed at 0.52 \(\upmu\)s using a 100 MHz system clock and had an average detection rate of 92%.
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Mavely, A.G., Judith, J.E., Sahal, P.A., Kuruvilla, S.A.: Eye gaze tracking based driver monitoring system. In: 11th IEEE International Conference on Circuits and Systems (ICCS), vol. 1, no. 3, pp. 364–367 (2017)
Lee, M.L., Howard, M.E., Horrey, W.J., Liang, Y., Anderson, C., Shreeve, M.S., O’Brien, C.S., Czeisler, C.A.: High risk of near-crash driving events following night-shift work. Proc. Natl. Acad. Sci. 113(1), 176–181 (2015)
Sun, Y., Yu, X.: An innovative nonintrusive driver assistance system for vital signal monitoring. IEEE Trans. Biomed. Health Inform. 18(6), 1932–1939 (2014)
Zhu, X.M., Zheng, W.L., Lu, B.L., Chen, X.P., Chen, S.G., Wang, C.H.: EOG-based drowsiness detection using convolutional neural networks. In: 10th IEEE International Conference on Neural Networks (IJCNN), vol. 6, no. 11, pp. 128–134 (2014)
Al-Rahayfeh, A., Faezipour, M.: Eye tracking and head movement detection: a state-of-art survey. IEEE J. Transl. Eng. Health Med. 1, 12 (2013)
Choi, I.H., Hong, S.H., Kim, Y.G.: Real-time categorization of driver’s gaze zone using the deep learning techniques. In: 2nd IEEE International Conference on Big Data and Smart Computing (BIGCOMP), vol. 18, no. 26, pp. 143–148 (2016)
Fridman, L., Lee, J., Reimer, B., Victor, V.: Owl and Lizard: patterns of head pose and eye pose in driver gaze classification. IET Comput. Vis. 10(4), 308–314 (2016)
Vicente, F., Huang, Z., Xiong, X., Torre, D.L.F., Zhang, W., Levi, D.: Driver gaze tracking and eyes Off the road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)
Zhai, F., Yang, Z., Song, Y., Ma, H.: A detection model for driver’s unsafe states based on real-time face-vision. In: 1st International Conference on Image Analysis and Signal Processing (IASP), vol. 4, pp. 12–14 (2010)
Xu, J., Min, J., Hu, J.: Real-time eye tracking for the assessment of driver fatigue. Healthc. Technol. Lett. 5(2), 54–58 (2018)
Hon, g, Qin, H.: Drivers drowsiness detection in embedded system. In: 2nd IEEE International Conference on Vehicular Electronics and Safety, vol. 5, pp. 13–15 (2007)
Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)
Liu, T., Yang, Y., Huang, G.B., Yeo, Y.K., Lin, Z.: Driver distraction detection using semi-supervised machine learning. IEEE Trans. Intell. Transp. Syst. 17(4), 1108–1120 (2016)
Said, S., AlKork, S., Beyrouthy, T., Hassan, M., Abdellatif, O., Abdraboo, M.F.: Real time eye tracking and detection- a driving assistance system. Adv. Sci. Technol. Eng. Syst. J. 3(6), 446–454 (2018)
Lai, H.C., Savvides, M., Chen, T.: Proposed FPGA hardware architecture for high frame rate (\(\gg\)100 fps) face detection using feature cascade classifiers. In: 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, vol. 16, pp. 27–29 (2007)
Chun, H., Papakonstantinou, A., Chen, D.: A novel SoC architecture on FPGA for ultra fast face detection. In: 27th IEEE International Conference on Computer Design (ICCD), vol. 4, no. 7, pp. 412–418 (2009)
Ishii, I., Ichida, T., Gu, Q., Takaki, T.: 500-fps face tracking system. J. Real Time Image Proc. 8(4), 379–388 (2013)
Yu, Y.H., L, T.T., Chen, P.Y., Kwok, N.: On-chip real-time feature extraction using semantic annotations for object recognition. J. Real Time Image Proc. 15(2), 249–264 (2018)
Sandnes, F.E., Neyse, L., Huang, Y.P.: Simple and practical skin detection with static RGB-color lookup tables: a visualization-based study. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), vol. 9, no. 12, pp. 2370–2375 (2016)
Huang, H., Guo, J.: Application of multiple order morphology transformation on edge detection for liver ct image. In: 7th IEEE International Conference on Information Technology and Artificial Intelligence Conference (ITAIC), vol. 20, no. 21, pp. 107–110 (2014)
Devadethan S., Titus G., Purushothaman S.: Face detection and facial feature extraction based on a fusion of knowledge based method and morphological image processing, In: International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 24–26 July 2014, pp. 5 (2014)
Guo, K., Sui, L., Qiu, J., Yu, J., Wang, J., Yao, S., Han, S., Wang, Y.: Angel-eye: a complete design flow for mapping CNN onto embedded FPGA. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 37(1), 35–47 (2018)
Ding, Z., Zhao, F., Wang, T., Shu, W., Wu, M.: Hecto-scale frame rate face detection system for SVGA source on FPGA board. In: IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), vol. 1, no. 3, pp. 37–40 (2011)
Qin, H., Xu, X., Hu, Z., Zhang, D.: Eye tracking system based on SOPC. In: 21th IEEE International Conference on Electronics, Circuits and Systems (ICECS), vol. 7, no. 10, pp. 171–174 (2014)
Kocić O., Simić A., Bjelica M.Z., Maruna T.:Optimization of driver monitoring ADAS algorithm for heterogeneous platform. In: 24th International Conference on Telecommunications Forum (TELFOR), 22–23 Nov 2016, pp. 4 (2016)
Supports from the Ministry of Science and Technology, under Grant MOST106-2221-E-194-060-MY2, Advanced Institute of Manufacturing with High-Tech Innovation (AIM-HI), and Center for Innovative Research on Aging Society (CIRAS) from The Featured Areas Research Center Program within the framework of Higher Education Sprout Project by the Ministry of Education (MoE) in Taiwan are gratefully acknowledged.
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Yu, Y., Ting, Y., Kwok, N. et al. High-speed gaze detection using a single FPGA for driver assistance systems. J Real-Time Image Proc (2020). https://doi.org/10.1007/s11554-020-01004-8
- Gaze detection
- FC neural networks